{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "胡润研究院发布《2020世茂深港国际中心•胡润全球富豪榜》"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from IPython.display import display, HTML\n",
    "URL_src = {\"url\":\"https://www.hurun.net/CN/Article/Details?num=9B764830B0B5\"}\n",
    "list = pd.read_html(URL_src[\"url\"],encoding=\"utf8\",header=0, index_col=0)  #编码方式为utf8，指定标题行，指定索引列\n",
    "print(len(list))   #len是指有几个html表格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>财富（亿元人民币）</th>\n",
       "      <th>财富变化%</th>\n",
       "      <th>公司</th>\n",
       "      <th>年龄</th>\n",
       "      <th>居住国</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>排名</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1-</th>\n",
       "      <td>杰夫·贝佐斯</td>\n",
       "      <td>9800</td>\n",
       "      <td>-5%</td>\n",
       "      <td>亚马逊</td>\n",
       "      <td>56</td>\n",
       "      <td>美国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2↑</th>\n",
       "      <td>伯纳德·阿诺特</td>\n",
       "      <td>7500</td>\n",
       "      <td>24%</td>\n",
       "      <td>酩悦·轩尼诗-路易·威登</td>\n",
       "      <td>70</td>\n",
       "      <td>法国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3↓</th>\n",
       "      <td>比尔·盖茨</td>\n",
       "      <td>7400</td>\n",
       "      <td>10%</td>\n",
       "      <td>微软</td>\n",
       "      <td>64</td>\n",
       "      <td>美国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4↓</th>\n",
       "      <td>沃伦·巴菲特</td>\n",
       "      <td>7100</td>\n",
       "      <td>16%</td>\n",
       "      <td>伯克希尔·哈撒韦</td>\n",
       "      <td>89</td>\n",
       "      <td>美国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-</th>\n",
       "      <td>马克·扎克伯格</td>\n",
       "      <td>5900</td>\n",
       "      <td>5%</td>\n",
       "      <td>Facebook</td>\n",
       "      <td>35</td>\n",
       "      <td>美国</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         姓名  财富（亿元人民币） 财富变化%            公司  年龄 居住国\n",
       "排名                                                \n",
       "1-   杰夫·贝佐斯       9800   -5%           亚马逊  56  美国\n",
       "2↑  伯纳德·阿诺特       7500   24%  酩悦·轩尼诗-路易·威登  70  法国\n",
       "3↓    比尔·盖茨       7400   10%            微软  64  美国\n",
       "4↓   沃伦·巴菲特       7100   16%      伯克希尔·哈撒韦  89  美国\n",
       "5-  马克·扎克伯格       5900    5%      Facebook  35  美国"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>居住地</th>\n",
       "      <th>人数</th>\n",
       "      <th>人数变化</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>居住地（城市）</th>\n",
       "      <th>人数.1</th>\n",
       "      <th>人数变化.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1 -</th>\n",
       "      <td>中国</td>\n",
       "      <td>799.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1 -</td>\n",
       "      <td>北京</td>\n",
       "      <td>110</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 -</th>\n",
       "      <td>美国</td>\n",
       "      <td>626.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2 -</td>\n",
       "      <td>纽约</td>\n",
       "      <td>98</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3↑</th>\n",
       "      <td>印度</td>\n",
       "      <td>137.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3↑</td>\n",
       "      <td>上海</td>\n",
       "      <td>83</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4↓</th>\n",
       "      <td>德国</td>\n",
       "      <td>122.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4↓</td>\n",
       "      <td>香港</td>\n",
       "      <td>76</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5↓</th>\n",
       "      <td>英国</td>\n",
       "      <td>119.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5↓</td>\n",
       "      <td>深圳</td>\n",
       "      <td>75</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    居住地     人数   人数变化  Unnamed: 4 Unnamed: 5 居住地（城市）  人数.1  人数变化.1\n",
       "1 -  中国  799.0  141.0         NaN        1 -      北京   110       7\n",
       "2 -  美国  626.0   42.0         NaN        2 -      纽约    98       6\n",
       "3↑   印度  137.0   33.0         NaN         3↑      上海    83      17\n",
       "4↓   德国  122.0    5.0         NaN         4↓      香港    76       7\n",
       "5↓   英国  119.0   10.0         NaN         5↓      深圳    75       8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th>人数变化</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1 -</th>\n",
       "      <td>北京</td>\n",
       "      <td>110</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>2↑</th>\n",
       "      <td>上海</td>\n",
       "      <td>83</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3↓</th>\n",
       "      <td>香港</td>\n",
       "      <td>76</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4↓</th>\n",
       "      <td>深圳</td>\n",
       "      <td>75</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5↑</th>\n",
       "      <td>广州</td>\n",
       "      <td>45</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    居住城市   人数  人数变化\n",
       "1 -   北京  110     7\n",
       "2↑    上海   83    17\n",
       "3↓    香港   76     7\n",
       "4↓    深圳   75     8\n",
       "5↑    广州   45    16"
      ]
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       "      <th>1.0</th>\n",
       "      <td>中国</td>\n",
       "      <td>790 (+140)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>新加坡</td>\n",
       "      <td>20 (-1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>美国</td>\n",
       "      <td>14 (+2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>菲律宾</td>\n",
       "      <td>9 (-1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>泰国</td>\n",
       "      <td>8 (-1)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     居住地    人数（人数变化）\n",
       "1.0   中国  790 (+140)\n",
       "2.0  新加坡     20 (-1)\n",
       "3.0   美国     14 (+2)\n",
       "4.0  菲律宾      9 (-1)\n",
       "5.0   泰国      8 (-1)"
      ]
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       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>财富（亿元人民币）</th>\n",
       "      <th>财富变化%</th>\n",
       "      <th>全球排名</th>\n",
       "      <th>主要财富来源</th>\n",
       "      <th>年龄</th>\n",
       "      <th>居住地</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1-</th>\n",
       "      <td>马云家族</td>\n",
       "      <td>3150</td>\n",
       "      <td>15%</td>\n",
       "      <td>21</td>\n",
       "      <td>阿里系</td>\n",
       "      <td>56</td>\n",
       "      <td>杭州</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-</th>\n",
       "      <td>马化腾</td>\n",
       "      <td>3080</td>\n",
       "      <td>16%</td>\n",
       "      <td>22</td>\n",
       "      <td>腾讯</td>\n",
       "      <td>49</td>\n",
       "      <td>深圳</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3-</th>\n",
       "      <td>许家印</td>\n",
       "      <td>2310</td>\n",
       "      <td>-11%</td>\n",
       "      <td>31</td>\n",
       "      <td>恒大</td>\n",
       "      <td>62</td>\n",
       "      <td>广州</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-</th>\n",
       "      <td>李嘉诚</td>\n",
       "      <td>2000</td>\n",
       "      <td>-3%</td>\n",
       "      <td>35</td>\n",
       "      <td>长江实业</td>\n",
       "      <td>92</td>\n",
       "      <td>香港</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4*</th>\n",
       "      <td>孙飘扬、钟慧娟夫妇</td>\n",
       "      <td>2000</td>\n",
       "      <td>155%</td>\n",
       "      <td>35</td>\n",
       "      <td>恒瑞医药、翰森制药</td>\n",
       "      <td>62, 59</td>\n",
       "      <td>连云港</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           姓名  财富（亿元人民币） 财富变化%  全球排名     主要财富来源      年龄  居住地\n",
       "1-       马云家族       3150   15%    21        阿里系      56   杭州\n",
       "2-        马化腾       3080   16%    22         腾讯      49   深圳\n",
       "3-        许家印       2310  -11%    31         恒大      62   广州\n",
       "4-        李嘉诚       2000   -3%    35       长江实业      92   香港\n",
       "4*  孙飘扬、钟慧娟夫妇       2000  155%    35  恒瑞医药、翰森制药  62, 59  连云港"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>新兴行业</th>\n",
       "      <th>上榜人数</th>\n",
       "      <th>代表人物（企业）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>先进制造</td>\n",
       "      <td>107</td>\n",
       "      <td>雷军（小米）、曾毓群（宁德时代）、龚虹嘉、陈春梅夫妇（海康威视）、周群飞、郑俊龙夫妇（蓝思科技）</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>大健康</td>\n",
       "      <td>53</td>\n",
       "      <td>孙飘扬、钟慧娟夫妇（恒瑞医药、翰森制药）、谢炳家族（中国生物制药）、李西廷（迈瑞）</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>传媒和娱乐</td>\n",
       "      <td>36</td>\n",
       "      <td>马化腾（腾讯）、丁磊、张志东（网易）、张一鸣（字节跳动）</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>应用软件</td>\n",
       "      <td>17</td>\n",
       "      <td>王文京（用友）、袁征（Zoom）、李仲初家族（石基）</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>电子商务</td>\n",
       "      <td>14</td>\n",
       "      <td>马云家族（阿里系）、黄峥（拼多多）、刘强东（京东）、蔡崇信（阿里巴巴）、王兴（美团点评）</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    新兴行业  上榜人数                                          代表人物（企业）\n",
       "1   先进制造   107  雷军（小米）、曾毓群（宁德时代）、龚虹嘉、陈春梅夫妇（海康威视）、周群飞、郑俊龙夫妇（蓝思科技）\n",
       "2    大健康    53         孙飘扬、钟慧娟夫妇（恒瑞医药、翰森制药）、谢炳家族（中国生物制药）、李西廷（迈瑞）\n",
       "3  传媒和娱乐    36                      马化腾（腾讯）、丁磊、张志东（网易）、张一鸣（字节跳动）\n",
       "4   应用软件    17                        王文京（用友）、袁征（Zoom）、李仲初家族（石基）\n",
       "5   电子商务    14      马云家族（阿里系）、黄峥（拼多多）、刘强东（京东）、蔡崇信（阿里巴巴）、王兴（美团点评）"
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       "                 姓名  财富（亿元人民币）       主要公司      年龄  居住国\n",
       "全球排名                                                  \n",
       "22          麦肯齐·贝索斯       3080        亚马逊      49   美国\n",
       "25         茱莉娅·科赫家族       2730       科氏工业      57   美国\n",
       "30          乔瓦尼·费列罗       2380      费列罗集团      55  比利时\n",
       "35        孙飘扬、钟慧娟夫妇       2000  恒瑞医药、翰森制药  62, 59   中国\n",
       "43    劳伦娜·鲍威尔·乔布斯家族       1680         苹果      56   美国"
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       "               人数                     过去五年财富增加最多的人\n",
       "过去五年财富创造                                          \n",
       "> 1000亿美元       1                           杰夫·贝佐斯\n",
       "500 - 1000亿美元   1                          伯纳德·阿诺特\n",
       "400 - 500亿美元    4  穆克什·安巴尼、史蒂夫·鲍尔默、马克·扎克伯格、麦肯齐·贝索斯\n",
       "300 - 400亿美元    5           埃隆·马斯克、谢尔盖·布林、迈克尔·布隆伯格\n",
       "200 - 300亿美元   10           弗朗索瓦·皮诺、马云、马化腾、爱丽丝·沃尔顿"
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       "           行业  占总人数%   比例变化 占总财富%      行业首富 主要公司\n",
       "1 -  科技、传媒和电信  12.7%   0.4%   18%    杰夫·贝佐斯  亚马逊\n",
       "2 -       房地产   9.6%  -0.2%  8.5%       许家印   恒大\n",
       "3↑        制造业   8.7%   0.4%  5.8%     何享健家族   美的\n",
       "4↓         投资   8.6%  -0.5%   11%     比尔·盖茨   微软\n",
       "5↓         零售   7.8%  -0.5%    9%  阿曼西奥·奥特加  蒂则诺"
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       "                                         公司    国家  年龄  2019财富（十亿美金）\n",
       "姓名                                                                 \n",
       "Christopher Cline  Foresight Energy Partner    美国  60           1.4\n",
       "Erramon Aboitiz     Aboitiz Equity Ventures   菲律宾  63           3.7\n",
       "Lars Larsen                            Jysk    丹麦  70           3.3\n",
       "David Koch                  Koch Industries    美国  79          47.0\n",
       "Lee Shin Cheng                    IOI Group  马来西亚  79           5.0"
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       "      前十名门槛（十亿美金）  前100名门槛（十亿美金）  前200名门槛（十亿美金）  前500名门槛（十亿美金）  前1000名门槛（十亿美金）\n",
       "2012           25            NaN            NaN            NaN             NaN\n",
       "2013           30           10.5            6.1            2.9             1.5\n",
       "2014           36           11.5            6.9            3.4             2.0\n",
       "2015           36           12.0            5.9            2.6             1.9\n",
       "2016           37           11.0            6.6            3.5             2.1"
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       "      <th>总财富（十亿美金）</th>\n",
       "      <th>首富</th>\n",
       "      <th>首富财富（十亿美金）</th>\n",
       "      <th>中国上榜人数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012*</th>\n",
       "      <td>83</td>\n",
       "      <td>18.2</td>\n",
       "      <td>1513</td>\n",
       "      <td>卡洛斯·斯利姆·埃卢家族</td>\n",
       "      <td>55</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>1453</td>\n",
       "      <td>3.7</td>\n",
       "      <td>5500</td>\n",
       "      <td>卡洛斯·斯利姆·埃卢家族</td>\n",
       "      <td>66</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>1867</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6900</td>\n",
       "      <td>比尔·盖茨</td>\n",
       "      <td>68</td>\n",
       "      <td>458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>2089</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6700</td>\n",
       "      <td>比尔·盖茨</td>\n",
       "      <td>85</td>\n",
       "      <td>478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>2189</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7369</td>\n",
       "      <td>比尔·盖茨</td>\n",
       "      <td>80</td>\n",
       "      <td>568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       总上榜人数  平均财富（十亿美金）  总财富（十亿美金）            首富  首富财富（十亿美金）  中国上榜人数\n",
       "2012*     83        18.2       1513  卡洛斯·斯利姆·埃卢家族          55       5\n",
       "2013    1453         3.7       5500  卡洛斯·斯利姆·埃卢家族          66     357\n",
       "2014    1867         3.7       6900         比尔·盖茨          68     458\n",
       "2015    2089         3.2       6700         比尔·盖茨          85     478\n",
       "2016    2189         3.0       7369         比尔·盖茨          80     568"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>占总人数%</th>\n",
       "      <th>上榜人数最多的国家/地区前三名 (%)</th>\n",
       "      <th>举例</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>创富指数</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33.6%</td>\n",
       "      <td>1.中国 (78.5%)</td>\n",
       "      <td>白手起家，没有父母的经济支持，如沃伦·巴菲特</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33.6%</td>\n",
       "      <td>2.美国 (8.8%)</td>\n",
       "      <td>白手起家，没有父母的经济支持，如沃伦·巴菲特</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33.6%</td>\n",
       "      <td>3.英国 (2.4%)</td>\n",
       "      <td>白手起家，没有父母的经济支持，如沃伦·巴菲特</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34%</td>\n",
       "      <td>1.美国 (35.5%)</td>\n",
       "      <td>白手起家，但获得父母一点帮助，如获得私立教育，例如马克·扎克伯格</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34%</td>\n",
       "      <td>2.俄罗斯 (8.6%)</td>\n",
       "      <td>白手起家，但获得父母一点帮助，如获得私立教育，例如马克·扎克伯格</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      占总人数% 上榜人数最多的国家/地区前三名 (%)                                举例\n",
       "创富指数                                                             \n",
       "5     33.6%        1.中国 (78.5%)            白手起家，没有父母的经济支持，如沃伦·巴菲特\n",
       "5     33.6%         2.美国 (8.8%)            白手起家，没有父母的经济支持，如沃伦·巴菲特\n",
       "5     33.6%         3.英国 (2.4%)            白手起家，没有父母的经济支持，如沃伦·巴菲特\n",
       "4       34%        1.美国 (35.5%)  白手起家，但获得父母一点帮助，如获得私立教育，例如马克·扎克伯格\n",
       "4       34%        2.俄罗斯 (8.6%)  白手起家，但获得父母一点帮助，如获得私立教育，例如马克·扎克伯格"
      ]
     },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名变化</th>\n",
       "      <th>姓名</th>\n",
       "      <th>财富（亿元人民币）</th>\n",
       "      <th>财富变化%</th>\n",
       "      <th>公司</th>\n",
       "      <th>年龄</th>\n",
       "      <th>居住国</th>\n",
       "      <th>居住城市</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>排名</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>杰夫·贝佐斯</td>\n",
       "      <td>9800</td>\n",
       "      <td>-5%</td>\n",
       "      <td>亚马逊</td>\n",
       "      <td>56</td>\n",
       "      <td>美国</td>\n",
       "      <td>西雅图</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>伯纳德·阿诺特</td>\n",
       "      <td>7500</td>\n",
       "      <td>24%</td>\n",
       "      <td>酩悦·轩尼诗-路易·威登</td>\n",
       "      <td>70</td>\n",
       "      <td>法国</td>\n",
       "      <td>巴黎</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1</td>\n",
       "      <td>比尔·盖茨</td>\n",
       "      <td>7400</td>\n",
       "      <td>10%</td>\n",
       "      <td>微软</td>\n",
       "      <td>64</td>\n",
       "      <td>美国</td>\n",
       "      <td>麦地那</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1</td>\n",
       "      <td>沃伦·巴菲特</td>\n",
       "      <td>7100</td>\n",
       "      <td>16%</td>\n",
       "      <td>伯克希尔·哈撒韦</td>\n",
       "      <td>89</td>\n",
       "      <td>美国</td>\n",
       "      <td>奥马哈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>马克·扎克伯格</td>\n",
       "      <td>5900</td>\n",
       "      <td>5%</td>\n",
       "      <td>Facebook</td>\n",
       "      <td>35</td>\n",
       "      <td>美国</td>\n",
       "      <td>帕洛阿尔托</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   排名变化       姓名  财富（亿元人民币） 财富变化%            公司  年龄 居住国   居住城市\n",
       "排名                                                            \n",
       "1     0   杰夫·贝佐斯       9800   -5%           亚马逊  56  美国    西雅图\n",
       "2     2  伯纳德·阿诺特       7500   24%  酩悦·轩尼诗-路易·威登  70  法国     巴黎\n",
       "3    -1    比尔·盖茨       7400   10%            微软  64  美国    麦地那\n",
       "4    -1   沃伦·巴菲特       7100   16%      伯克希尔·哈撒韦  89  美国    奥马哈\n",
       "5     0  马克·扎克伯格       5900    5%      Facebook  35  美国  帕洛阿尔托"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for df in list:  #利用循环遍历获取数据\n",
    "    display(df.head())   #df.head()默认只读取钱买你的五行，也可以在括号里输入你想要的行数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>姓名</th>\n",
       "      <th>财富（亿元人民币）</th>\n",
       "      <th>财富变化%</th>\n",
       "      <th>公司</th>\n",
       "      <th>年龄</th>\n",
       "      <th>居住国</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-</td>\n",
       "      <td>杰夫·贝佐斯</td>\n",
       "      <td>9800</td>\n",
       "      <td>-5%</td>\n",
       "      <td>亚马逊</td>\n",
       "      <td>56</td>\n",
       "      <td>美国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2↑</td>\n",
       "      <td>伯纳德·阿诺特</td>\n",
       "      <td>7500</td>\n",
       "      <td>24%</td>\n",
       "      <td>酩悦·轩尼诗-路易·威登</td>\n",
       "      <td>70</td>\n",
       "      <td>法国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3↓</td>\n",
       "      <td>比尔·盖茨</td>\n",
       "      <td>7400</td>\n",
       "      <td>10%</td>\n",
       "      <td>微软</td>\n",
       "      <td>64</td>\n",
       "      <td>美国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4↓</td>\n",
       "      <td>沃伦·巴菲特</td>\n",
       "      <td>7100</td>\n",
       "      <td>16%</td>\n",
       "      <td>伯克希尔·哈撒韦</td>\n",
       "      <td>89</td>\n",
       "      <td>美国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5-</td>\n",
       "      <td>马克·扎克伯格</td>\n",
       "      <td>5900</td>\n",
       "      <td>5%</td>\n",
       "      <td>Facebook</td>\n",
       "      <td>35</td>\n",
       "      <td>美国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6↑</td>\n",
       "      <td>阿曼西奥·奥特加</td>\n",
       "      <td>5700</td>\n",
       "      <td>45%</td>\n",
       "      <td>蒂则诺</td>\n",
       "      <td>83</td>\n",
       "      <td>西班牙</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7↓</td>\n",
       "      <td>卡洛斯·斯利姆·埃卢家族</td>\n",
       "      <td>5000</td>\n",
       "      <td>9%</td>\n",
       "      <td>墨西哥美洲电信公司</td>\n",
       "      <td>80</td>\n",
       "      <td>墨西哥</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8-</td>\n",
       "      <td>谢尔盖·布林</td>\n",
       "      <td>4800</td>\n",
       "      <td>26%</td>\n",
       "      <td>谷歌</td>\n",
       "      <td>46</td>\n",
       "      <td>美国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9↑</td>\n",
       "      <td>拉里·佩奇</td>\n",
       "      <td>4690</td>\n",
       "      <td>26%</td>\n",
       "      <td>谷歌</td>\n",
       "      <td>46</td>\n",
       "      <td>美国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9↓</td>\n",
       "      <td>穆克什·安巴尼</td>\n",
       "      <td>4690</td>\n",
       "      <td>24%</td>\n",
       "      <td>瑞来斯</td>\n",
       "      <td>62</td>\n",
       "      <td>印度</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9*</td>\n",
       "      <td>史蒂夫·鲍尔默</td>\n",
       "      <td>4690</td>\n",
       "      <td>63%</td>\n",
       "      <td>微软</td>\n",
       "      <td>63</td>\n",
       "      <td>美国</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    排名            姓名  财富（亿元人民币） 财富变化%            公司  年龄  居住国\n",
       "0   1-        杰夫·贝佐斯       9800   -5%           亚马逊  56   美国\n",
       "1   2↑       伯纳德·阿诺特       7500   24%  酩悦·轩尼诗-路易·威登  70   法国\n",
       "2   3↓         比尔·盖茨       7400   10%            微软  64   美国\n",
       "3   4↓        沃伦·巴菲特       7100   16%      伯克希尔·哈撒韦  89   美国\n",
       "4   5-       马克·扎克伯格       5900    5%      Facebook  35   美国\n",
       "5   6↑      阿曼西奥·奥特加       5700   45%           蒂则诺  83  西班牙\n",
       "6   7↓  卡洛斯·斯利姆·埃卢家族       5000    9%     墨西哥美洲电信公司  80  墨西哥\n",
       "7   8-        谢尔盖·布林       4800   26%            谷歌  46   美国\n",
       "8   9↑         拉里·佩奇       4690   26%            谷歌  46   美国\n",
       "9   9↓       穆克什·安巴尼       4690   24%           瑞来斯  62   印度\n",
       "10  9*       史蒂夫·鲍尔默       4690   63%            微软  63   美国"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = list[0].copy().reset_index()   #只读取第一个表格，reset_index可以还原索引，重新变为默认的整型索引 \n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <td>酩悦·轩尼诗-路易·威登</td>\n",
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       "      <th>2</th>\n",
       "      <td>3↓</td>\n",
       "      <td>比尔·盖茨</td>\n",
       "      <td>7400</td>\n",
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       "      <td>微软</td>\n",
       "      <td>64</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4↓</td>\n",
       "      <td>沃伦·巴菲特</td>\n",
       "      <td>7100</td>\n",
       "      <td>16%</td>\n",
       "      <td>伯克希尔·哈撒韦</td>\n",
       "      <td>89</td>\n",
       "      <td>美国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5-</td>\n",
       "      <td>马克·扎克伯格</td>\n",
       "      <td>5900</td>\n",
       "      <td>5%</td>\n",
       "      <td>Facebook</td>\n",
       "      <td>35</td>\n",
       "      <td>美国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6↑</td>\n",
       "      <td>阿曼西奥·奥特加</td>\n",
       "      <td>5700</td>\n",
       "      <td>45%</td>\n",
       "      <td>蒂则诺</td>\n",
       "      <td>83</td>\n",
       "      <td>西班牙</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0  1-    杰夫·贝佐斯  9800  -5%           亚马逊  56   美国\n",
       "1  2↑   伯纳德·阿诺特  7500  24%  酩悦·轩尼诗-路易·威登  70   法国\n",
       "2  3↓     比尔·盖茨  7400  10%            微软  64   美国\n",
       "3  4↓    沃伦·巴菲特  7100  16%      伯克希尔·哈撒韦  89   美国\n",
       "4  5-   马克·扎克伯格  5900   5%      Facebook  35   美国\n",
       "5  6↑  阿曼西奥·奥特加  5700  45%           蒂则诺  83  西班牙"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.columns = d.iloc[0] # 将第一行内容值作成栏位值\n",
    "d = d[1:] # 将第一行内容忽然不留，且每运行一次就消失第一行\n",
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2↑', '伯纳德·阿诺特', 7500, '24%', '酩悦·轩尼诗-路易·威登', 70, '法国'], dtype='object', name=1)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.columns  #显示第一行的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2↑', '伯纳德·阿诺特', 7500, '24%', '酩悦·轩尼诗-路易·威登', 70, '法国']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "[str(0) if x!=x else x for i,x in enumerate(d.columns)]  #条件控制（不太懂）\n",
    " #enumerate() 函数用于将一个可遍历的数据对象组合为一个索引序列，同时列出数据和数据下标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2↑', '伯纳德·阿诺特', 7500, '24%', '酩悦·轩尼诗-路易·威登', 70, '法国'], dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i=0\n",
    "d.columns = [str(i) if x!=x else x for i,x in enumerate(d.columns)]    #（不太懂）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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